Spatial Contraction Based on Velocity Variation for Natural Walking in Virtual Reality.

Virtual Reality (VR) offers an immersive 3D digital environment, but enabling natural walking sensations without the constraints of physical space remains a technological challenge. Previous VR locomotion methods, including game controller, teleportation, treadmills, walking-in-place, and redirected walking (RDW), have made strides towards overcoming this challenge. However, these methods also face limitations such as possible unnaturalness, additional hardware requirements, or motion sickness risks. This paper introduces "Spatial Contraction (SC)", an innovative VR locomotion method inspired by the phenomenon of Lorentz contraction in Special Relativity. Similar to the Lorentz contraction, our SC contracts the virtual space along the user's velocity direction in response to velocity variation. The virtual space contracts more when the user's speed is high, whereas minimal or no contraction happens at low speeds. We provide a virtual space transformation method for spatial contraction and optimize the user experience in smoothness and stability. Through SC, VR users can effectively traverse a longer virtual distance with a shorter physical walking. Different from locomotion gains, the spatial contraction effect is observable by the user and aligns with their intentions, so there is no inconsistency between the user's proprioception and visual perception. SC is a general locomotion method that has no special requirements for VR scenes. The experimental results of our live user studies in various virtual scenarios demonstrate that SC has a significant effect in reducing both the number of resets and the physical walking distance users need to cover. Furthermore, experiments have also demonstrated that SC has the potential for integration with existing locomotion techniques such as RDW.

Multi-User Redirected Walking in Separate Physical Spaces for Online VR Scenarios.

With the recent rise of Metaverse, online multiplayer VR applications are becoming increasingly prevalent worldwide. However, as multiple users are located in different physical environments, different reset frequencies and timings can lead to serious fairness issues for online collaborative/competitive VR applications. For the fairness of online VR apps/games, an ideal online RDW strategy must make the locomotion opportunities of different users equal, regardless of different physical environment layouts. The existing RDW methods lack the scheme to coordinate multiple users in different PEs, and thus have the issue of triggering too many resets for all the users under the locomotion fairness constraint. We propose a novel multi-user RDW method that is able to significantly reduce the overall reset number and give users a better immersive experience by providing a fair exploration. Our key idea is to first find out the "bottleneck" user that may cause all users to be reset and estimate the time to reset given the users' next targets, and then redirect all the users to favorable poses during that maximized bottleneck time to ensure the subsequent resets can be postponed as much as possible. More particularly, we develop methods to estimate the time of possibly encountering obstacles and the reachable area for a specific pose to enable the prediction of the next reset caused by any user. Our experiments and user study found that our method outperforms existing RDW methods in online VR applications.

BiRD: Using Bidirectional Rotation Gain Differences to Redirect Users during Back-and-forth Head Turns in Walking.

Redirected walking (RDW) facilitates user navigation within expansive virtual spaces despite the constraints of limited physical spaces. It employs discrepancies between human visual-proprioceptive sensations, known as gains, to enable the remapping of virtual and physical environments. In this paper, we explore how to apply rotation gain while the user is walking. We propose to apply a rotation gain to let the user rotate by a different angle when reciprocating from a previous head rotation, to achieve the aim of steering the user to a desired direction. To apply the gains imperceptibly based on such a Bidirectional Rotation gain Difference (BiRD), we conduct both measurement and verification experiments on the detection thresholds of the rotation gain for reciprocating head rotations during walking. Unlike previous rotation gains which are measured when users are turning around in place (standing or sitting), BiRD is measured during users' walking. Our study offers a critical assessment of the acceptable range of rotational mapping differences for different rotational orientations across the user's walking experience, contributing to an effective tool for redirecting users in virtual environments.

The current status and challenges of perioperative management of patients with a BMI of greater than or equal to 50 kg/m 2 undergoing bariatric surgery in China: a multicenter cross-sectional study.

BACKGROUND: Performing bariatric surgery on patients with a BMI of over 50 kg/m 2 is challenging. This study aimed to explore the status and challenges related to the perioperative management of such patients in China. MATERIALS AND METHODS: A prospective survey was designed to investigate the perioperative management of patients with a BMI of greater than or equal to 50 kg/m 2 undergoing bariatric surgery in China. The questionnaire of our survey included general information, preoperative management measures, surgical procedures performed, technical details regarding anaesthesia, and postoperative management measures. A response from only one attending physician per bariatric centre was accepted. RESULTS: Physicians from a total of 101 hospitals responded to the questionnaire, and the questionnaire data from 98 hospitals were complete. These centres had completed a total of 44 702 bariatric surgeries since the launch of such surgery to December 2021. A total of 3280 patients had a BMI exceeding 50 kg/m 2 . The preferred surgical procedures for patients with super obesity were sleeve gastrectomy by 62 centres, Roux-en-Y gastric bypass by 11 centres, sleeve gastrectomy plus jejunojejunal bypass by 19 centres, one anastomosis gastric bypass by 1 centre, and duodenal switch by 1 centre. The most worrying issues were cardiopulmonary failure and difficulty in extubation. 91 centres believed that preoperative weight loss was beneficial. A low-calorie diet was the specific measure mainly implemented, only three centres considered using intragastric balloon placement. Postoperative management measures varied greatly. CONCLUSION: Bariatric surgery has seen rapid development. Chinese physicians show significant differences regarding the perioperative management for patients with a BMI of over 50 kg/m 2 . The perioperative risks of these patients remain relatively high, making further development of clinical pathways is necessary.

GP-Recon: Online Monocular Neural 3D Reconstruction with Geometric Prior.

High-fidelity online 3D scene reconstruction from monocular videos continues to be challenging, especially for coherent and fine-grained geometry reconstruction. The previous learning-based online 3D reconstruction approaches with neural implicit representations have shown a promising ability for coherent scene reconstruction, but often fail to consistently reconstruct fine-grained geometric details during online reconstruction. This paper presents a new on-the-fly monocular 3D reconstruction approach, named GP-Recon, to perform high-fidelity online neural 3D reconstruction with fine-grained geometric details. We incorporate geometric prior (GP) into a scene's neural geometry learning to better capture its geometric details and, more importantly, propose an online volume rendering optimization to reconstruct and maintain geometric details during the online reconstruction task. The extensive comparisons with state-of-the-art approaches show that our GP-Recon consistently generates more accurate and complete reconstruction results with much better fine-grained details, both quantitatively and qualitatively.

One-Step Out-of-Place Resetting for Redirected Walking in VR.

Redirected walking (RDW) allows users to explore virtual environments in limited physical spaces by imperceptibly steering them away from obstacles and space boundaries. However, even with those techniques, the risk of collision cannot always be avoided. For such situations, resetting techniques have been proposed to provide an immediate adjustment of the physical walking direction of a user. Existing resetting techniques are either applied in-place, where the user changes orientation but stays in the same position or out-of-place methods where the user is guided to move from the current position to a safe location all while freezing the movement in the virtual world. While out-of-place methods have the potential to provide more freedom to user movements after resetting, current out-of-place methods do not provide enough guidance for the users to move to optimal locations. In this work, we propose a novel out-of-place resetting strategy that guides users to optimal physical locations with the most potential for free movement and a smaller amount of resetting required for their further movements. For this purpose, we calculate a heat map of the walking area according to the average walking distance using a simulation of the currently used RDW algorithm. Based on this heat map, we identify the most suitable position for a one-step reset within a predefined searching range and use this one as the reset point. Our results show that our method increases the average moving distance within one cycle of resetting. Furthermore, our resetting method can be applied to any physical area with obstacles. That means that RDW methods that were not suitable for such environments (e.g., Steer to Center) combined with our resetting can also be extended to such complex walking areas. In addition, we present a user interface to provide a similar visual experience between these methods, using a two-arrows indicator to help users adjust their position and direction.

On Rotation Gains Within and Beyond Perceptual Limitations for Seated VR.

Head tracking in head-mounted displays (HMDs) enables users to explore a 360-degree virtual scene with free head movements. However, for seated use of HMDs such as users sitting on a chair or a couch, physically turning around 360-degree is not possible. Redirection techniques decouple tracked physical motion and virtual motion, allowing users to explore virtual environments with more flexibility. In seated situations with only head movements available, the difference of stimulus might cause the detection thresholds of rotation gains to differ from that of redirected walking. Therefore we present an experiment with a two-alternative forced-choice (2AFC) design to compare the thresholds for seated and standing situations. Results indicate that users are unable to discriminate rotation gains between 0.89 and 1.28, a smaller range compared to the standing condition. We further treated head amplification as an interaction technique and found that a gain of 2.5, though not a hard threshold, was near the largest gain that users consider applicable. Overall, our work aims to better understand human perception of rotation gains in seated VR and the results provide guidance for future design choices of its applications.

StructNeRF: Neural Radiance Fields for Indoor Scenes With Structural Hints.

Neural Radiance Fields (NeRF) achieve photo-realistic view synthesis with densely captured input images. However, the geometry of NeRF is extremely under-constrained given sparse views, resulting in significant degradation of novel view synthesis quality. Inspired by self-supervised depth estimation methods, we propose StructNeRF, a solution to novel view synthesis for indoor scenes with sparse inputs. StructNeRF leverages the structural hints naturally embedded in multi-view inputs to handle the unconstrained geometry issue in NeRF. Specifically, it tackles the texture and non-texture regions respectively: a patch-based multi-view consistent photometric loss is proposed to constrain the geometry of textured regions; for non-textured ones, we explicitly restrict them to be 3D consistent planes. Through the dense self-supervised depth constraints, our method improves both the geometry and the view synthesis performance of NeRF without any additional training on external data. Extensive experiments on several real-world datasets demonstrate that StructNeRF shows superior or comparable performance compared to state-of-the-art methods (e.g. NeRF, DSNeRF, RegNeRF, Dense Depth Priors, MonoSDF, etc.) for indoor scenes with sparse inputs both quantitatively and qualitatively.

Learning Implicit Glyph Shape Representation.

Automatic generation of fonts can greatly facilitate the font design process, and provide prototypes where designers can draw inspiration from. Existing generation methods are mainly built upon rasterized glyph images to utilize the successful convolutional architecture, but ignore the vector nature of glyph shapes. We present an implicit representation, modeling each glyph as shape primitives enclosed by several quadratic curves. This structured implicit representation is shown to be better suited for glyph modeling, and enables rendering glyph images at arbitrary high resolutions. Our representation gives high-quality glyph reconstruction and interpolation results, and performs well on the challenging one-shot font style transfer task comparing to other alternatives both qualitatively and quantitatively.

SceneViewer: Automating Residential Photography in Virtual Environments.

Selecting views is one of the most common but overlooked procedures in topics related to 3D scenes. Typically, existing applications and researchers manually select views through a trial-and-error process or "preset" a direction, such as the top-down views. For example, literature for scene synthesis requires views for visualizing scenes. Research on panorama and VR also require initial placements for cameras, etc. This article presents SceneViewer, an integrated system for automatic view selections. Our system is achieved by applying rules of interior photography, which guides potential views and seeks better views. Through experiments and applications, we show the potentiality and novelty of the proposed method.

Adaptive Optimization Algorithm for Resetting Techniques in Obstacle-Ridden Environments.

Redirected Walking (RDW) algorithms aim to impose several types of gains on users immersed in Virtual Reality and distort their walking paths in the real world, thus enabling them to explore a larger space. Since collision with physical boundaries is inevitable, a reset strategy needs to be provided to allow users to reset when they hit the boundary. However, most reset strategies are based on simple heuristics by choosing a seemingly suitable solution, which may not perform well in practice. In this article, we propose a novel optimization-based reset algorithm adaptive to different RDW algorithms. Inspired by the approach of finite element analysis, our algorithm splits the boundary of the physical world by a set of endpoints. Each endpoint is assigned a reset vector to represent the optimized reset direction when hitting the boundary. The reset vectors on the edge will be determined by the interpolation between two neighbouring endpoints. We conduct simulation-based experiments for three RDW algorithms with commonly used reset algorithms to compare with. The results demonstrate that the proposed algorithm significantly reduces the number of resets.

SceneDirector: Interactive Scene Synthesis by Simultaneously Editing Multiple Objects in Real-Time.

Intelligent tools for creating synthetic scenes have been developed significantly in recent years. Existing techniques on interactive scene synthesis only incorporate a single object at every interaction, i.e., crafting a scene through a sequence of single-object insertions with user preferences. These techniques suggest objects by considering existent objects in the scene instead of fully picturing the eventual result, which is inherently problematic since the sets of objects to be inserted are seldom fixed during interactive processes. In this article, we introduce SceneDirector, a novel interactive scene synthesis tool to help users quickly picture various potential synthesis results by simultaneously editing groups of objects. Specifically, groups of objects are rearranged in real-time with respect to a position of an object specified by a mouse cursor or gesture, i.e., a movement of a single object would trigger the rearrangement of the existing object group, the insertions of potentially appropriate objects, and the removal of redundant objects. To achieve this, we first propose an idea of coherent group set which expresses various concepts of layout strategies. Subsequently, we present layout attributes, where users can adjust how objects are arranged by tuning the weights of the attributes. Thus, our method gives users intuitive control of both how to arrange groups of objects and where to place them. Through extensive experiments and two applications, we demonstrate the potentiality of our framework and how it enables concurrently effective and efficient interactions of editing groups of objects.

Smoothness preserving layout for dynamic labels by hybrid optimization.

Stable label movement and smooth label trajectory are critical for effective information understanding. Sudden label changes cannot be avoided by whatever forced directed methods due to the unreliability of resultant force or global optimization methods due to the complex trade-off on the different aspects. To solve this problem, we proposed a hybrid optimization method by taking advantages of the merits of both approaches. We first detect the spatial-temporal intersection regions from whole trajectories of the features, and initialize the layout by optimization in decreasing order by the number of the involved features. The label movements between the spatial-temporal intersection regions are determined by force directed methods. To cope with some features with high speed relative to neighbors, we introduced a force from future, called temporal force, so that the labels of related features can elude ahead of time and retain smooth movements. We also proposed a strategy by optimizing the label layout to predict the trajectories of features so that such global optimization method can be applied to streaming data. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at 10.1007/s41095-021-0231-y.

User-Guided Deep Human Image Matting Using Arbitrary Trimaps.

Image matting is widely studied for accurate foreground extraction. Most algorithms, including deep-learning based solutions, require a carefully edited trimap. Recent works attempt to combine the segmentation stage and matting stage in one CNN model, but errors occurring at the segmentation stage lead to unsatisfactory matte. We propose a user-guided approach for practical human matting. More precisely, we provide a good automatic initial matting and a natural way of interaction that reduces the workload of drawing trimaps and allows users to guide the matting in ambiguous situation. We also combine the segmentation and matting stage in an end-to-end CNN architecture and introduce a residual-learning module to support convenient stroke-based interaction. The proposed model learns to propagate the input trimap and modify the deep image features, which can efficiently correct the segmentation errors. Our model supports arbitrary forms of trimaps from carefully edited to totally unknown maps. Our model also allows users to choose from different foreground estimations according to their preference. We collected a large human matting dataset consisting of 12K real-world human images with complex background and human-object relations. The proposed model is trained on the new dataset with a novel trimap generation strategy that enables the model to tackle different test situations and highly improves the interaction efficiency. Our method outperforms other state-of-the-art automatic methods and achieve competitive accuracy when high-quality trimaps are provided. Experiments indicate that our interactive matting strategy is superior to separately estimating the trimap and alpha matte using two models.

Towards Better Caption Supervision for Object Detection.

As training high-performance object detectors requires expensive bounding box annotations, recent methods resort to free-available image captions. However, detectors trained on caption supervision perform poorly because captions are usually noisy and cannot provide precise location information. To tackle this issue, we present a visual analysis method, which tightly integrates caption supervision with object detection to mutually enhance each other. In particular, object labels are first extracted from captions, which are utilized to train the detectors. Then, the objects detected from images are fed into caption supervision for further improvement. To effectively loop users into the object detection process, a node-link-based set visualization supported by a multi-type relational co-clustering algorithm is developed to explain the relationships between the extracted labels and the images with detected objects. The co-clustering algorithm clusters labels and images simultaneously by utilizing both their representations and their relationships. Quantitative evaluations and a case study are conducted to demonstrate the efficiency and effectiveness of the developed method in improving the performance of object detectors.

Making Resets away from Targets: POI aware Redirected Walking.

Rapidly developing Redirected Walking (ROW) technologies have enabled VR applications to immerse users in large virtual environments (VE) while actually walking in relatively small physical environments (PE). When an unavoidable collision emerges in a PE, the ROW controller suspends the user's immersive experience and resets the user to a new direction in PE. Existing ROW methods mainly aim to reduce the number of resets. However, from the perspective of the user experience, when users are about to reach a point of interest (POI) in a VE, reset interruptions are more likely to have an impact on user experience. In this paper, we propose a new ROW method, aiming to keep resets occurring at a longer distance from the virtual target, as well as to reduce the number of resets. Simulation experiments and real user studies demonstrate that our method outperforms state-of-the-art ROW methods in the number of resets and dramatically increases the distance between the reset locations and the virtual targets.

Fast 3D Indoor Scene Synthesis by Learning Spatial Relation Priors of Objects.

We present a framework for fast synthesizing indoor scenes, given a room geometry and a list of objects with learnt priors. Unlike existing data-driven solutions, which often learn priors by co-occurrence analysis and statistical model fitting, our method measures the strengths of spatial relations by tests for complete spatial randomness (CSR), and learns discrete priors based on samples with the ability to accurately represent exact layout patterns. With the learnt priors, our method achieves both acceleration and plausibility by partitioning the input objects into disjoint groups, followed by layout optimization using position-based dynamics (PBD) based on the Hausdorff metric. Experiments show that our framework is capable of measuring more reasonable relations among objects and simultaneously generating varied arrangements in seconds compared with the state-of-the-art works.

Context-Consistent Generation of Indoor Virtual Environments Based on Geometry Constraints.

In this article, we propose a system that can automatically generate immersive and interactive virtual reality (VR) scenes by taking real-world geometric constraints into account. Our system can not only help users avoid real-world obstacles in virtual reality experiences, but also provide context-consistent contents to preserve their sense of presence. To do so, our system first identifies the positions and bounding boxes of scene objects as well as a set of interactive planes from 3D scans. Then context-consistent virtual objects that have similar geometric properties to the real ones can be automatically selected and placed into the virtual scene, based on learned object association relations and layout patterns from large amounts of indoor scene configurations. We regard virtual object replacement as a combinatorial optimization problem, considering both geometric and contextual consistency constraints. Quantitative and qualitative results show that our system can generate plausible interactive virtual scenes that highly resemble real environments, and have the ability to keep the sense of presence for users in their VR experiences.

Efficacy and safety of one anastomosis gastric bypass versus Roux-en-Y gastric bypass for type 2 diabetes remission (ORDER): protocol of a multicentre, randomised controlled, open-label, superiority trial.

INTRODUCTION: Previous studies have demonstrated that one anastomosis gastric bypass (OAGB) is not inferior to Roux-en-Y gastric bypass (RYGB) in treating obesity. However, high level evidence comparing the efficacy and safety of both procedures in type 2 diabetes (T2D) treatment is still lacking, which is another main aim of bariatric surgery. The presented trial has been designed to aim at investigating the superiority of OAGB over the reference procedure RYGB in treating T2D as primary endpoint. And diabetes-related microvascular and macrovascular complications, cardiovascular comorbidities, weight loss, postoperative nutritional status, quality of life and overall complications will be followed up for 5 years as secondary endpoints. METHODS AND ANALYSIS: This prospective, multicentre, randomised superiority open-label trial will be conducted in patients of Asian descent. A total of 248 patients (BMI≥27.5 kg/m2) who are diagnosed with T2D will be randomly assigned (1:1) to OAGB or RYGB with blocks of four. The primary endpoint is the complete diabetes remission rate defined as HbA1c≤6.0% and fasting plasma glucose≤5.6 mmol/L without any antidiabetic medications at 1 year after surgery. All secondary endpoints will be measured at different follow-up visit points, which will start at least 3 months after enrolment, with a continuous annual follow-up for five postoperative years in order to provide solid evidence on the efficacy and safety of OAGB in patients with T2D. ETHICS AND DISSEMINATION: The study has been approved by the ethics committee of leading centre (Beijing Friendship Hospital, Capital Medical University, no. 2021-P2-037-03). The results generated from this work will be disseminated to academic audiences and the public via publications in international peer-reviewed journals and conferences. The data presented will be imported into a national data registry. Findings are expected to be available in 2025, which will facilitate clinical decision-making in the field. TRIAL REGISTRATION NUMBER: NCT05015283.

The Choice of Gastric Bypass or Sleeve Gastrectomy for Patients Stratified by Diabetes Duration and Body Mass Index (BMI) level: Results from a National Registry and Meta-analysis.

PURPOSE: To determine whether sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) should be the optimal choice in patients stratified by diabetes duration and body mass index (BMI) level. METHODS: Classification tree analysis was performed to identify the influential factors for surgical procedure selection in real setting. Meta-analyses stratified by influential factors were conducted to compare the complete diabetes remission rates between SG and RYGB. The cost-effectiveness analysis was performed when results from meta-analysis remain uncertain. RESULTS: Among 3198 bariatric procedures in China, 824 (73%) SGs and 191 (17%) RYGBs were performed in patients with T2DM. Diabetes duration with a cutoff value of 5 years and BMI level with 35.5 kg/m2 were identified as the influential factors. For patients with diabetes duration > 5 years, RYGB showed a significant higher complete diabetes remission rate than SG at 1 year: 0.52 (95% confidence interval (CI): 0.46-0.58) versus 0.36 (95% CI: 0.30-0.42). For patients with diabetes duration ≤ 5 years and BMI ≥ 35.5 kg/m2, there was no significant difference between 2 procedures: 0.57 (95% CI: 0.43-0.71) for SG versus 0.66 (95% CI: 0.62-0.70) for RYGB. The cost-effectiveness ratios of SG and RYGB were 244.58 and 276.97 dollars per QALY, respectively. CONCLUSIONS: For patients with diabetes duration > 5 years, RYGB was the optimal choice with regard to achieving complete diabetes remission at 1 year after surgery. However, for patients with diabetes duration ≤ 5 years and BMI ≥ 35.5 kg/m2, SG appeared to provide a cost-effective choice.